93 research outputs found
Inter-tier Interference Suppression in Heterogeneous Cloud Radio Access Networks
Incorporating cloud computing into heterogeneous networks, the heterogeneous
cloud radio access network (H-CRAN) has been proposed as a promising paradigm
to enhance both spectral and energy efficiencies. Developing interference
suppression strategies is critical for suppressing the inter-tier interference
between remote radio heads (RRHs) and a macro base station (MBS) in H-CRANs. In
this paper, inter-tier interference suppression techniques are considered in
the contexts of collaborative processing and cooperative radio resource
allocation (CRRA). In particular, interference collaboration (IC) and
beamforming (BF) are proposed to suppress the inter-tier interference, and
their corresponding performance is evaluated. Closed-form expressions for the
overall outage probabilities, system capacities, and average bit error rates
under these two schemes are derived. Furthermore, IC and BF based CRRA
optimization models are presented to maximize the RRH-accessed users' sum rates
via power allocation, which is solved with convex optimization. Simulation
results demonstrate that the derived expressions for these performance metrics
for IC and BF are accurate; and the relative performance between IC and BF
schemes depends on system parameters, such as the number of antennas at the
MBS, the number of RRHs, and the target signal-to-interference-plus-noise ratio
threshold. Furthermore, it is seen that the sum rates of IC and BF schemes
increase almost linearly with the transmit power threshold under the proposed
CRRA optimization solution
Fronthaul-Constrained Cloud Radio Access Networks: Insights and Challenges
As a promising paradigm for fifth generation (5G) wireless communication
systems, cloud radio access networks (C-RANs) have been shown to reduce both
capital and operating expenditures, as well as to provide high spectral
efficiency (SE) and energy efficiency (EE). The fronthaul in such networks,
defined as the transmission link between a baseband unit (BBU) and a remote
radio head (RRH), requires high capacity, but is often constrained. This
article comprehensively surveys recent advances in fronthaul-constrained
C-RANs, including system architectures and key techniques. In particular, key
techniques for alleviating the impact of constrained fronthaul on SE/EE and
quality of service for users, including compression and quantization,
large-scale coordinated processing and clustering, and resource allocation
optimization, are discussed. Open issues in terms of software-defined
networking, network function virtualization, and partial centralization are
also identified.Comment: 5 Figures, accepted by IEEE Wireless Communications. arXiv admin
note: text overlap with arXiv:1407.3855 by other author
Energy-Efficient Resource Allocation Optimization for Multimedia Heterogeneous Cloud Radio Access Networks
The heterogeneous cloud radio access network (H-CRAN) is a promising paradigm
which incorporates the cloud computing into heterogeneous networks (HetNets),
thereby taking full advantage of cloud radio access networks (C-RANs) and
HetNets. Characterizing the cooperative beamforming with fronthaul capacity and
queue stability constraints is critical for multimedia applications to
improving energy efficiency (EE) in H-CRANs. An energy-efficient optimization
objective function with individual fronthaul capacity and inter-tier
interference constraints is presented in this paper for queue-aware multimedia
H-CRANs. To solve this non-convex objective function, a stochastic optimization
problem is reformulated by introducing the general Lyapunov optimization
framework. Under the Lyapunov framework, this optimization problem is
equivalent to an optimal network-wide cooperative beamformer design algorithm
with instantaneous power, average power and inter-tier interference
constraints, which can be regarded as the weighted sum EE maximization problem
and solved by a generalized weighted minimum mean square error approach. The
mathematical analysis and simulation results demonstrate that a tradeoff
between EE and queuing delay can be achieved, and this tradeoff strictly
depends on the fronthaul constraint
Group Sparse Precoding for Cloud-RAN with Multiple User Antennas
Cloud radio access network (C-RAN) has become a promising network
architecture to support the massive data traffic in the next generation
cellular networks. In a C-RAN, a massive number of low-cost remote antenna
ports (RAPs) are connected to a single baseband unit (BBU) pool via high-speed
low-latency fronthaul links, which enables efficient resource allocation and
interference management. As the RAPs are geographically distributed, the group
sparse beamforming schemes attracts extensive studies, where a subset of RAPs
is assigned to be active and a high spectral efficiency can be achieved.
However, most studies assumes that each user is equipped with a single antenna.
How to design the group sparse precoder for the multiple antenna users remains
little understood, as it requires the joint optimization of the mutual coupling
transmit and receive beamformers. This paper formulates an optimal joint RAP
selection and precoding design problem in a C-RAN with multiple antennas at
each user. Specifically, we assume a fixed transmit power constraint for each
RAP, and investigate the optimal tradeoff between the sum rate and the number
of active RAPs. Motivated by the compressive sensing theory, this paper
formulates the group sparse precoding problem by inducing the -norm as
a penalty and then uses the reweighted heuristic to find a solution.
By adopting the idea of block diagonalization precoding, the problem can be
formulated as a convex optimization, and an efficient algorithm is proposed
based on its Lagrangian dual. Simulation results verify that our proposed
algorithm can achieve almost the same sum rate as that obtained from exhaustive
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